Artificial Intelligence is being supplanted by "Artificial Brain," i.e. neuromorphic technologies. Yet there still a whopping gap that neuromorphic systems need to close before they will become a match for successful AI applications.

1. Future with AI
The Artificial Brain Odyssey
Dr. Giedrius Tomas Burachas,
Intentiva Inc.

2. I and my brain
100 billion neurons, 300 billion connections in 1cm3 = # stars in Milky way

3. AI: predictions,
hopes and reality

4. ...but what is AI?
John McCarthy (1955, roots in Lithuania) suggested:
AI is a science and engineering aimed at creating
intelligent machines
But what is an “intelligent machine”?
Turing test (1950): a machine can reason if:
It can imitate humans so that an average person
would assume he/she is dealing with another person
New definitions:
Jeff Hawkins: an intelligent system can correctly predict what’s
coming. Created hierarchical temporal memory (Numenta, Grok;
Vicarious Systems)
Dr. Kestas Kveraga, Harvard University: visual brain systems use
predictive feedback loops to speed up object recognition
Penti Haikonen (Nokia): intelligent systems can predict outcomes of
their actions
Imagination is required in order to simulate consequences of
choosing alternative actions. Igor Aleksander: computers with
imagination

5. Achievements of AI
Playing chess:
IBM’s Deep Blue computer defeated the world champion
Garry Kasparov in 1997.
Used simple search algorithm
Triumph of pure AI: no inspiration from ther brain
Speech synthesis:
Apple popularized it 25 years ago with Macintosh.
computer algorithms have trouble pronouncing irregular words.
Terrence Sejnowski, Salk professor, in 1987 created a neural net Nettalk that learned to pronounce
them in a way like human baby.
Showed advantages of brain-like algorithms.
Face detection:
Face detection algorithm is ubiquitous in photo cameras.
Based on the algorithm invented by MIT professor Paul Viola.
Used inspiration from visaul system of primate brain. Had many conversations with him
Face recogniiton:
A harder problem, but several companies solve it successfully, including Dr. Algimantas Malickas’
company in Vilnius: Neurotechnology.
Relies on AI methods (machine learning, such as SVMs)

6. Achievements of AI, cnt’d
Voice recognition:
Nuance Dragon used in speech transcription, e.g. medicine.
Google Voice – suitable for transcribing 70-80% voice messages.
Weakness: break down in presence of accent, background noise.
Language understanding:
IBM Watson won “Jeopardy” game (2011)
Largest project – Stanford Research Institute’s “personal assistant” (DARPA).
SRI system was commercialized and Apple bought it in 2011: SIRI app for iPhone.
Maintaining dialogue: No machine has passed the Turing test
Recognition of human actions and intentions:
Simple algorithms for human action recognition in security tapes
(San Diego)
Numenta (Vicarious ?): uses brain- inspired principles for human action detection,
and other applications.
Intentiva: recognizes human intentions
Recognizing emotional and social states:
San Diego company Emotient recognizes emotions from facial expressions.

7. AI vs. Artificial Brain
- Principle of operation: logical rules created
by engineers
- Works with standard digital computers
- Software is separated from hardware
- Memory is separate from CPU
- Advantage: the logics of operation is
straightforward to understand
- Disadvantages:
- Limited tolerance for errors
- Needs a lot of energy for each
computational cycle
- Principle of operation: adaptive processes,
statistical learning rules
- Requires neuromorphic electronics
- Software is merged with hardware
- Each processor has its own memory
- Advantages:
- Tolerance to errors and malfunctions,
ability to compensate them
- Asynchronous parallel operation needs
little energy
- Disadvantage: unexpected emergent
phenomena can arise
- e.g.: artificial “epilepsy”

8. When computers and AI surpass
human intellect?
Ray Kurzweil

9. Robotics:
realizing brain-inspired
algorithms in computers
UCSD robot child
Recognizes and reacts
to human emotions
All modern robots run on
traditional CPUs/GPUs
EB project “Artificial curiosity”:
robots with internal motivation for
learning
Robots
“animats”
Emotional/social
robots
Robot training imitates development of a baby
Robobee

10. The Brain
Levels
Cells: neurons
Neuronal circuits
Nuclei, “maps”
Functional systems
(e.g., vision, audition, speech,
motivation, decision-making,
motor)
Global behavioral systems
Function
Integration of information and
feature representation
Neuronal algorithms
(amplification, WTA, sparse
code)
Support a function
Processes info for supporting
elements of behavior
Choreography of behaviors,
planning
Churchland & Sejnowski

11. Neurons-
most complex cells
Neuron’s job: integrate spikes coming from
other neurons via synapses and decide
whether to generate a spike
- Spikes are on or off
- Spike influence is determined by synaptic
weights and synchrony
- Excitation and inhibition are balanced
Buračas et al.

12. Neuronal circuits –
info processing units
Most famous neuronal circuit: macrocolumn of cortex,
Decision-making unit
Minicolumn: ~80 neurons
Macrocolumn = 100 minicolumns (0.5mm)
Blue Brain project: Dr. Henry Markram
EU project >1 bln Euros, Lausanne &
Heidelberg univ. (86 institutions, 10 years)
Evolved Machines: Dr. Paul Rhodes

13. Cortical “maps” & functional systems
Kveraga et al.
Visual system
2 mln macrocolumns in human cortex
Prediction
mechanisms
speed up
object
recognition

14. Brain and behavior
Brain rhythms
Excitation/suppressio
n
Sleep/waking cycle
Motivation
Foraging behavior
Fight/Flee responses
Reproductive
behavior
Universal neurochemistry of love: dopamine->testosterone->occytocin

15. Cartography of human brain (fMRI)
Language
Introspection
Speech generation Speech
understanding
The same parts of the brain are
activated when observing, dreaming
and imagining the same scene/object
Network for Introspection (yellow) Brain/mind reading:

16. Neuromorphic
systems
SyNAPSE – the most ambitious program for building neuromorphic systems
(Systems of Neuromorphic Adaptive Plastic Scalable Electronics)
DARPA, HP, HRL, IBM and 5 universities (2009-now: ~$100M)
Neurosynaptic chips Neurosynaptic processors
Goal:
Brain-like chips and systems
with
- 10 bln neurons
- 100 trln synapses
- 1kW
- 2 liters of volume
achievements:
- 2009 cat brain-sized system
simulated on Blue Gene
- 2011 a chip with 256 nrn, 250K
snps
- Not finished: 2013 multi-chip
neuroprocessor system (1M nrn on
each chip)

17. Why neuromorphic
systems are coming?
The brain is more effective energetically than computers by a factor of 1mln – 1bln

18. Neuromorphic chips
SyNAPSE DARPA chip:
256 neurons (digital)
262K programmable synapses
65K trainable synapses
NeuroGrid, Stenford Uv, 2009:
1 mln. Neurons
6 bln. Synapses
Real time(10 spikes/sek)
2W!
But not trainable...
Neuroinformatics Institute, ETH, Zurich,
2013:
neuromorphic trainable chip
Silicone physics used to simulate
neuronal biophysics
Implements “liquid states” just like the
brain
Qualcomm and The Brain Corporation
(>$40 mln):
Secretive, however, Qualcomm is building
a chip
Dr.E. Izhikevich
Dr. Rodney
Douglas
Dr. Quabena
Boahen
Dr. Dharmendra
Modha
Schematics of the digital
artificial neuron

19. Applications of neuromorphic systems
Applications for vision:
Several neuromorphic chips connected serially by analogy to human visual
system: can attend to interesting objects in the visual field.

20. Neuroscience is moving towards
neuroindustry: spiking camera

21. Conclusions
Machine learning/neural network research has been
pushing the limits of computer vision and AI, but to
date it has been mostly based on traditional von
Neumann type computer architectures
As the amount of data and complexity of the systems
grows, the need for neuromorphic solutions
becomes more apparent
Since neuromorphic systems as of 2014 are still very far
from being of practical value, interim solutions take
the shape of ASICs dedicated for machine learning
tasks (e.g. Nervana Systems, San Diego)
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